Efficient Indexing for Strongly Similar Image Retrieval

Proceedings of the Fourth Canadian Conference on Computer and Robot Vision (CRV 2007), 2007

Abstract

Strongly similar subimages contain different views of thesame object. In subimage search, the user selects an imageregion and the retrieval system attempts to find matchingsubimages in an image database that are strongly similar.Solutions have been proposed using salient featuresor ?interest points? that have associated descriptor vectors.However, searching large image databases by exhaustivecomparison of interest point descriptors is not feasible.To solve this problem, we propose a novel off-line indexingscheme based on the most significant bits (MSBs) of thesedescriptors. On-line search uses this index file to limit thesearch to interest points whose descriptors have the sameMSB value, a process up to three orders of magnitude fasterthan exhaustive search. It is also incremental, since the indexfile for a union of a group of images can be created bymerging the index files of the individual image groups. Theeffectiveness of the approach is demonstrated experimentallyon a variety of image databases.

Publication date

2007

Language

English

Affiliation

NRC Institute for Information Technology; National Research Council Canada